University of Colorado Boulder

Association Rules Analysis

This course is part of Data Analysis with Python Specialization

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

22 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection

  • Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.

  • Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Details to know

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Assessments

4 quizzes, 1 assignment

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

22 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Analysis with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 5 modules in this course

This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.

What's included

2 videos4 readings1 quiz

This week we will briefly discuss association rule mining, such as closed and maxed patterns.

What's included

1 video1 quiz

This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.

What's included

2 videos4 readings1 quiz1 discussion prompt

Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.

What's included

1 video2 readings1 quiz1 discussion prompt

The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.

What's included

1 reading1 assignment1 discussion prompt

Instructor

Di Wu
University of Colorado Boulder
15 Courses29,139 learners

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